Generally speaking, face-tracking refers to a computer vision technology that extracts the shapes of human faces in arbitrary digital images. It detects facial features and ignores anything else in surrounding, such as furniture or dogs. According to the related art, there are many conventional face tracking methods (e.g., snake, AAM, CLM . . . , etc.) based on face detection to detect face region and then set an initial shape (which is composed by feature points) inside the region, and the content of a given part in face region of an image is extracted to get features and then go fine tuning the face shape to fit features in the image face.
However, these methods may result in false shape extractions due to over/under face region detection or target-like background noises, and the following processes (e.g., the power saving application or the camera application) based on the face detection results would be affected by the false shape extractions. Therefore, there is a need for an innovative face-tracking scheme which is capable of extracting face shapes accurately.